Cluster-based Adaptive Retrieval: Dynamic Context Selection for RAG Applications

• URL: http://arxiv.org/abs/2511.14769v1
• Date: Thu, 02 Oct 2025 05:11:12 GMT
• Title: Cluster-based Adaptive Retrieval: Dynamic Context Selection for RAG Applications
• Authors: Yifan Xu, Vipul Gupta, Rohit Aggarwal, Varsha Mahadevan, Bhaskar Krishnamachari,
• Abstract summary: Cluster-based Adaptive Retrieval (CAR) is an algorithm that determines the optimal number of documents by analyzing the clustering patterns of ordered query-document similarity distances.<n>CAR consistently picks the optimal retrieval depth and achieves the highest TES score, outperforming every fixed top-k baseline.
• Score: 8.946586077722822
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) enhances large language models (LLMs) by pulling in external material, document, code, manuals, from vast and ever-growing corpora, to effectively answer user queries. The effectiveness of RAG depends significantly on aligning the number of retrieved documents with query characteristics: narrowly focused queries typically require fewer, highly relevant documents, whereas broader or ambiguous queries benefit from retrieving more extensive supporting information. However, the common static top-k retrieval approach fails to adapt to this variability, resulting in either insufficient context from too few documents or redundant information from too many. Motivated by these challenges, we introduce Cluster-based Adaptive Retrieval (CAR), an algorithm that dynamically determines the optimal number of documents by analyzing the clustering patterns of ordered query-document similarity distances. CAR detects the transition point within similarity distances, where tightly clustered, highly relevant documents shift toward less pertinent candidates, establishing an adaptive cut-off that scales with query complexity. On Coinbase's CDP corpus and the public MultiHop-RAG benchmark, CAR consistently picks the optimal retrieval depth and achieves the highest TES score, outperforming every fixed top-k baseline. In downstream RAG evaluations, CAR cuts LLM token usage by 60%, trims end-to-end latency by 22%, and reduces hallucinations by 10% while fully preserving answer relevance. Since integrating CAR into Coinbase's virtual assistant, we've seen user engagement jump by 200%.

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